Understanding the Binding and Structures in Model Complexes of Polypeptides and Cofactors
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Competitive binding between metal cofactors and functional groups of polypeptides results in a diversity of structures and chemistries in metalloproteins. Herein, we examined elements of this competitive binding using [metal(auxiliary ligand)(peptide)] complexes, where the metal(auxiliary ligand) combinations are Cu II (terpy) 2+, Co III (salen) +, and Fe III (salen) + and the peptides are either the dipeptide arginine–tyrosine (RY) or the tripeptide arginine–tyrosine–glycine (RYG). Structural diversity was established and substantiated via tandem mass spectrometry, with and without peptide derivatization and substitution. All the complexes dissociated to give high abundances of the peptide radical cations, but the structures of these ions differ depending on the composition of the preceding metal complex. Density functional theory calculations provided insights into different binding modes within the complexes and also provided details of the mechanisms by which different [RY] •+ and [RYG] •+ ions fragment. Infrared multiple-photon dissociation spectroscopy established that [Cu(terpy)RYG] 2+ is bound through the carboxylate group, but calculations showed that it can convert to the phenolate-bound structure under a low-energy barrier. Despite the variety and apparent complexity in binding, the overall chemistry could be characterized using intrinsic acid–base chemistry and the concept of hard/soft Lewis acids/bases. The resulting complex structures were experimentally probed and were found to be in accordance with predictions. For the complexes, the drive toward energy minimization can take several pathways that involve multiple functional groups, thereby leading to a rich chemistry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".